Phylogenetic Tree

Load packages

library(tidyverse)
library(gridExtra)
library(cowplot)
library(viridis)
library(ggridges)
library(ggstance)
library(treeio)
library(ggtree)
library(tidytree)

Load and combine data

studies <- read_csv("../data/studies_gsheet.csv")
species <- read_csv("../data/species_gsheet.csv")
fulltree <- read.nexus("../data/consensusTree_10kTrees_298Primates_V3.nex")
refs <- read_csv("../data/ref_nodes.csv")

data <- studies %>% 
  left_join(species, by = c("species" = "species_file")) %>% 
  rename(label = species_tree, num = n) %>% 
  select(label, studyID, species, site, num)

data2 <- data %>% group_by(label, species) %>% summarise(totalN = sum(num, na.rm = T))

# turn tree into tidy dataframe
tree2 <- fulltree %>% 
  drop.tip(c("Pan_troglodytes_schweinfurthii", "Pan_troglodytes_troglodytes",
             "Pan_troglodytes_vellerosus", "Pongo_pygmaeus")) %>%
  as_tibble

inner_nodes <- c(295:304, 314:315, 320:326, 328:332, 334, 336:353, 356:358, 362:364, 366, 382:390, 395, 399:402, 404:410, 412:418, 420:423, 430:433, 435:439, 445:449, 453:455, 457:458, 459:461, 464:471, 473:478, 480:484, 488:499, 512, 531:532, 536:538, 541:545, 552:554, 561:563, 566, 568)

tree3 <- tree2 %>% 
  mutate(label = fct_recode(label, "Pan_troglodytes" = "Pan_troglodytes_verus",
                                   "Pongo_spp" = "Pongo_abelii")) %>% 
  left_join(data2) %>% 
  mutate(
    hasN = ifelse(is.na(totalN), .1, .5), # used to size branches + color the tip labels
    hasN2 = ifelse(is.na(totalN) & !(node %in% inner_nodes), 0, .5), # used to color branches
    label = str_replace_all(label, "_", " ")) %>% 
  left_join(refs) %>% 
  groupClade(refs$node[-1]) %>% 
  mutate(group = fct_recode(group, "2" = "1"))

Joining, by = "label"
Column `label` joining factor and character vector, coercing into character vectorJoining, by = "node"

# turn back into tree
tree4 <- as.treedata(tree3)

Circular tree of the 10ktree primates

cols <- viridis(4, end = .9)

p <- ggtree(tree4, aes(size = hasN, alpha = hasN2), layout = "circular") +
  # highlight clades with background colors
  geom_hilight(node = 485, fill = cols[1], alpha = .3) +
  geom_hilight(node = 488, fill = cols[1], alpha = .3) +
  geom_hilight(node = 421, fill = cols[2], alpha = .3) +
  geom_hilight(node = 299, fill = cols[3], alpha = .3) +
  geom_hilight(node = 404, fill = cols[4], alpha = .3) +
  # plot tree again to be on top of the highlights
  geom_tree() +
  # root
  geom_rootpoint(size = 1) +
  # tips
  geom_tippoint(aes(size = totalN), alpha = .7) +
  geom_tiplab2(aes(alpha = hasN), offset = 3, size = 3) +
  # tweak scales
  scale_alpha_continuous(range = c(.2, 1)) +
  scale_size_continuous(range = c(.5, 15)) +
  # widen plotting area
  xlim(NA, 100)

p

ggsave("../graphs/phylo_full.pdf", p, width = 8, height = 8, scale = 2)

# to figure out node numbers
n1 <- p + geom_text(aes(label = node, x = branch), size = 2, col = "blue", vjust = -.5)
ggsave("../graphs/full_tree_nodes_circular.pdf", n1, width = 8, height = 8, scale = 2)

n2 <- ggtree(tree4, aes(size = hasN, alpha = hasN2)) +
  # highlight clades with background colors
  geom_hilight(node = 485, fill = cols[1], alpha = .3) +
  geom_hilight(node = 488, fill = cols[1], alpha = .3) +
  geom_hilight(node = 421, fill = cols[2], alpha = .3) +
  geom_hilight(node = 299, fill = cols[3], alpha = .3) +
  geom_hilight(node = 404, fill = cols[4], alpha = .3) +
  # plot tree again to be on top of the highlights
  geom_tree() +
  # root
  geom_rootedge(rootedge = 2) +
  geom_rootpoint(size = 1) +
  # node labels
  geom_text(aes(label = node, x = branch), size = 2, col = "blue", vjust = -.5) +
  # tips
  geom_tippoint(aes(size = totalN), alpha = .7) +
  geom_tiplab(aes(alpha = hasN), offset = 1.8, size = 3) +
  # tweak scales
  scale_alpha_continuous(range = c(.2, 1)) +
  scale_size_continuous(range = c(.5, 15)) +
  # widen plotting area
  expand_limits(x = 90) +
  theme_tree2()

ggsave("../graphs/full_tree_nodes.pdf", n2, width = 8, height = 20, scale = 2)

Sample size in detail

data %>% arrange(desc(num)) %>% mutate(site = str_sub(site, 1, 35)) %>% select(-species)

# subset tree to just those species who have sample sizes reported, i.e. those who were tested
to_drop <- tree3 %>% filter(is.na(totalN)) %>% pull(label)
tree5 <- drop.tip(tree4, to_drop)
d3 <- mutate(data, label = str_replace_all(label, "_", " "))

# filter super large samples out for visualization? note in caption
# species with more than X sites can get a density
d3a <- d3 %>% group_by(species) %>% filter(n_distinct(site) >= 4, num <= 200)
d3b <- d3 %>% # setdiff(d3, d3a) %>% ## <- to NOT show points for densities
  group_by(species) %>% 
  # create variable num2 is NA if there's only one data point for a species
  # --> those species will only get the vertical crossbar
  mutate(flag = n_distinct(site) == 1) %>% 
  ungroup %>% 
  mutate(num2 = ifelse(flag, NA, num)) %>% 
  filter(num <= 200)

# for vertical crossbar = median
d4a <- d3 %>% 
  group_by(label, species) %>% 
  summarise(Mdn = median(num, na.rm = T)) # totalN = sum(num), sitesN = n_distinct(site)

# for cross = median (sample size by species and site)
d4b <- d3 %>% 
  group_by(label, species, site) %>% 
  summarise(Mdn_site = median(num, na.rm = T))%>% 
  group_by(label, species) %>% 
  summarise(Mdn_site2 = median(Mdn_site))

# make NA when closer than 1 to Mdn
d4 <- full_join(d4a, d4b) %>% 
  mutate(Mdn_site2 = ifelse(abs(Mdn - Mdn_site2) < 1, NA, Mdn_site2))

Joining, by = c("label", "species")

# for vertical line in ridge plot (grand median)
# + hacky way to make horizontal grid lines for right panel only
v <- tibble(reference = c(NA, median(d3$num, na.rm = T)), .panel = c("Tree", "xSample size"))
h <- tibble(reference = c(NA, 1:Ntip(tree5)), .panel = c("Tree", rep("xSample size", Ntip(tree5))))

# for axis labels
ax <- tibble(lab = c("Distance (Millions of years)", "Sample size"), 
             x = c(60, 100), y = -4, .panel = c("Tree", "xSample size"))

# LEFT FACET
q <- ggtree(tree5, aes(col = group)) +
  # root
  geom_rootedge(rootedge = 5) +
  # tip labels
  geom_tippoint(aes(size = totalN), alpha = .5) +
  geom_tiplab(offset = 4, size = 3) +
  # tweak scales
  scale_color_manual(values = c("grey30", cols)) +
  scale_fill_manual(values = cols) + # when all categories are taken: cols
  # display timescale at the bottom
  theme_tree2() +
  xlim_tree(112) +
  xlim_expand(c(0, 175), "xSample size") +
  # add axis labels
  geom_text(data = ax, aes(label = lab), col = "black") +
  scale_x_continuous(expand = expand_scale(mult = c(0, .01))) +
  scale_y_continuous(limits = c(2, Ntip(tree5)), oob = function(x, ...) x) +
  coord_cartesian(clip = "off") +
  # add reference lines (these will show up on right panel of facet_plot only)
  geom_hline(data = h, aes(yintercept = reference), lwd = .2, col = "grey", alpha = .5) +
  geom_vline(data = v, aes(xintercept = reference), lwd = 1.5, col = "grey", alpha = .3) +
  # remove facet strips, expand bottom margin (to make space for x axis labels)
  theme(strip.text = element_blank(), strip.background = element_blank(),
        plot.margin = unit(c(1, 1, 2, 1.5), "cm"), panel.spacing = unit(1, "cm"))

# right-side viz depends on the number of sites per species:
# 1 site = vertical crossbar only
# 2+ sites = points + crossbar at median
# X+ sites = densities (currently, X = 4 just to illustrate)

# dirty hack: x in front of "Sample size" is to have that panel sort to the right (alphabetically) until I figure out why it doesn't just go by order. This cropped up as an issue when I added the dummy point for the x-axis expansion...

# ADD RIGHT FACET
q %>% 
  # densities for species with enough sites
  facet_plot("xSample size", d3a, geom_density_ridges, 
             aes(x = num, group = label, fill = group, height = ..density..),
             alpha = .5, lwd = .3, scale = .3) %>%
  # vertical crossbar for Mdn, X for Mdn of site medians
  facet_plot("xSample size", d4, geom_crossbarh, aes(x = Mdn, xmin = Mdn, xmax = Mdn, group = label,
             col = group), alpha = .5, width = .6, fatten = 1.5) %>%
  facet_plot("xSample size", d4, geom_point, aes(x = Mdn_site2, group = label), shape = 4, size = 2.5,
             stroke = 1.05) %>%
  # vertical mark for individual sites
  facet_plot("xSample size", d3b, geom_jitter, aes(x = num2, group = label), shape = "|", size = 2.5,
             width = .1, height = 0, alpha = .8)

ggsave("../graphs/phylo_ridge_site.pdf", width = 6, height = 6, scale = 2)

# ADD RIGHT FACET
q %>% 
  facet_plot("xSample size", filter(d3, num < 200), geom_boxploth, aes(x = num, group = label, 
             fill = group), alpha = .5, lwd = .3, varwidth = T) %>% 
  # vertical crossbar for Mdn
  facet_plot("xSample size", d4, geom_crossbarh, aes(x = Mdn, xmin = Mdn, xmax = Mdn, group = label,
             col = group), alpha = .5, width = .6, fatten = 1.5)

ggsave("../graphs/phylo_ridge_box.pdf", width = 6, height = 8, scale = 2)

Session info

sessionInfo()

R version 3.6.1 (2019-07-05)
Platform: x86_64-apple-darwin15.6.0 (64-bit)
Running under: macOS Mojave 10.14.5

Matrix products: default
BLAS:   /System/Library/Frameworks/Accelerate.framework/Versions/A/Frameworks/vecLib.framework/Versions/A/libBLAS.dylib
LAPACK: /Library/Frameworks/R.framework/Versions/3.6/Resources/lib/libRlapack.dylib

locale:
[1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8

attached base packages:
[1] stats     graphics  grDevices utils     datasets  methods   base     

other attached packages:
 [1] tidytree_0.2.8    ggtree_1.16.6     treeio_1.8.2      ggstance_0.3.3    ggridges_0.5.1   
 [6] viridis_0.5.1     viridisLite_0.3.0 cowplot_1.0.0     gridExtra_2.3     forcats_0.4.0    
[11] stringr_1.4.0     dplyr_0.8.3       purrr_0.3.2       readr_1.3.1       tidyr_1.0.0      
[16] tibble_2.1.3      ggplot2_3.2.1     tidyverse_1.2.1  

loaded via a namespace (and not attached):
 [1] tidyselect_0.2.5   xfun_0.10          reshape2_1.4.3     haven_2.1.1        lattice_0.20-38   
 [6] colorspace_1.4-1   vctrs_0.2.0        generics_0.0.2     htmltools_0.4.0    base64enc_0.1-3   
[11] yaml_2.2.0         rlang_0.4.0        pillar_1.4.2       glue_1.3.1         withr_2.1.2       
[16] modelr_0.1.5       readxl_1.3.1       rvcheck_0.1.5      lifecycle_0.1.0    plyr_1.8.4        
[21] munsell_0.5.0      gtable_0.3.0       cellranger_1.1.0   rvest_0.3.4        evaluate_0.14     
[26] labeling_0.3       knitr_1.25         parallel_3.6.1     broom_0.5.2        Rcpp_1.0.2        
[31] BiocManager_1.30.7 scales_1.0.0       backports_1.1.5    jsonlite_1.6       digest_0.6.21     
[36] hms_0.5.1          stringi_1.4.3      grid_3.6.1         cli_1.1.0          tools_3.6.1       
[41] magrittr_1.5       lazyeval_0.2.2     crayon_1.3.4       ape_5.3            pkgconfig_2.0.3   
[46] zeallot_0.1.0      xml2_1.2.2         lubridate_1.7.4    rmarkdown_1.16     assertthat_0.2.1  
[51] httr_1.4.1         rstudioapi_0.10    R6_2.4.0           nlme_3.1-141       compiler_3.6.1